To effectively use Python for data science and machine learning, a solid grasp of certain language features beyond the basics is necessary. This chapter revisits core concepts and introduces intermediate techniques often applied in data-focused programming.

We will cover list comprehensions and generator expressions for creating sequences efficiently. You'll work with iterators and generators to process data streams using less memory. We will examine advanced function arguments (*args, **kwargs), decorators for modifying function behavior, and context managers (with statements) for managing resources. Additionally, we'll look at object-oriented programming principles useful in structuring ML projects and best practices for handling exceptions.

By the end of this chapter, you will be better prepared to write cleaner, more efficient Python code for data analysis and preparation tasks.